Triple
T7063679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Corps législatif |
E164286
|
entity |
| Predicate | meetingsWere |
P74810
|
FINISHED |
| Object | non-public |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: non-public | Statement: [Corps législatif, meetingsWere, non-public]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meetingsWere Context triple: [Corps législatif, meetingsWere, non-public]
-
A.
meetingsAre
Indicates that certain entities function as or are classified as meetings in relation to one another.
-
B.
hasMeeting
Indicates that one entity is scheduled to participate in or hold a meeting with another entity or at a specific time or place.
-
C.
meetingType
Indicates the specific category or format of a meeting that characterizes how it is organized or conducted.
-
D.
meets
Indicates that two or more entities come together at the same place and time, typically for interaction or a shared purpose.
-
E.
convenesDuring
Indicates that one entity formally gathers or brings together another entity or group during a specified time period or event.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c688796c148190adb2f1596f595f22 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4a3c36c819080942c59f1830ae8 |
completed | March 27, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69c6e1bdc1f08190975fcdbbb1854d1e |
completed | March 27, 2026, 7:59 p.m. |
| PDg | Predicate description generation | batch_69c6e4a15b088190bee9a23e94aaac53 |
completed | March 27, 2026, 8:12 p.m. |
Created at: March 27, 2026, 2:38 p.m.